This the last article in a series of four. The previous article illustrated that future investment returns through any investment horizon shorter than Warren Buffett’s “forever” are typically dominated by expectations at that horizon for a company’s long-term cash flows. The second article illustrated that cases for future company cash flow are influenced by the cases for business drivers that the first article illustrated.
But we’re still missing something. Now we are relying on cases for long-term cash flows expectations at earlier horizons, and those are influenced by expectations at earlier horizons for the events that drive those long-term cash flows.
Expectations for events can drive what investors put into their price-setting rationale at an investment horizon. Typically these expectations are not certain but probabilistic. While investors typically hesitate to write probabilistic expectations explicitly because that is impractical without tools like the Bullet Point Network Platform, some investors are indeed explicit in specific domains where it is more practical because investments are dominated by only a few probabilities.
Two of these domains are event-driven investing and biotechnology, so let’s start by looking at them and then considering how investors extend this approach into areas with more uncertainties.
BPN’s co-founders cut their teeth at Goldman Sachs while its Senior Partner was Robert Rubin, who expanded the firm’s merger arbitrage business into a broader “event-driven investing” style before becoming US Treasury Secretary. Interviewed in Goldman Sachs; The Culture of Success, Rubin said of this investing style:
You had to stick to your discipline and try to reduce everything to plusses and minuses and to probabilities . . . It was a high-risk business, but I’ll tell you, it did teach you to think of life in terms of probabilities instead of absolutes. You couldn’t be in that business and not internalize that probabilistic approach to life.
Event-driven investors try to pay prices explicitly based on their probability of different outcomes to uncertainties like the closing of a merger deal:
Rubin was known for his focus on how future stories might change his reduction of stories to probabilities. His Deputy Treasury Secretary Larry Summers, who later became President of Harvard University, described this focus:
Rubin ends half the meetings with, 'So we don't have to make a decision on this today, do we?' New information will evolve.
We can see this ongoing re-pricing in stock price charts of merger targets as new stories change investors’ reduction to probabilities:
Like event-driven investors, some biotech investors write price targets explicitly based on their probability of the success of future events. Typically for them, success is not deal closing but product launch with regulatory approval.
[Example of simple price formula from Kezar report by William Blair]
Certainly, to set these probabilities, investors can unpack them into probabilities for earlier events that influence final approval. There is even abundant historical data on the frequencies of success in one stage conditional on success in the previous stage.
[Smallest table from that BIO report]
But each new drug is different from the average drug in its class, so investors seek to apply experience in reducing stories to probabilities as they read and listen to the views of researchers, regulators, insurers, doctors, and patients.
[A few words on how stories evolve followed by a Chart of how price change with one or two stories annotated on it.]
But in practice, how can we anticipate ahead of time how those inputs to investors’ price setting may evolve over time so we can anticipate how their price may evolve?
Calling Odds on How Odds will Evolve
With the multiple time dimensions that we described in the previous article, we can produce cases for how those biotech investors, for example, may change their expected odds of outcomes and the timing of those outcomes.
[Illustration of odds moving over time, maybe cases first and then percentiles]
To do this realistically in practice, there are nuances to consider, including what billionaire investor George Soros terms the “reflexivity” of expectations over time, with expectations increases in one period often increasing the odds of expectations increases in the next period, and vice versa for expectations decreases.
Atop the Bullet Point Network Platform, we have built an Event Model that considers that nuance and others, making it practical for us all to focus on our views about the eventual odds, applying the best practices detailed in our first article, and then the Event Model will help us explore cases for how expectations may change over time between what is priced in now and what eventually happens.
[Illustration entering some odds.]
Together with the Platform’s models for company long-term cash flows and valuation, as illustrated in the previous article, this Event Model enables us all to translate our insights on future events, from product launches to politics, into cases for company cash flow and valuation.
[Illustration of valuation moving over time alongside cash balance moving over time]
This can enable managers, and the investors who back them, to make better decisions about how much money to raise and to spend based on how and when valuation might change in the future as cash balance changes. Doing this better repeatedly can make a major improvement in managers’ and investors' career success.
This all starts with organizing stories continually, as illustrated in the first article, to help us all use our human insight to question and enhance our quantitative scenarios continually.
That’s how we all can Be More Strategic by Connecting Stories to Statistics.